Turning a private GraphQL API into a public one comes with unexpected challenges. We’ll share how we approached this transition—starting from an existing internal schema that wasn’t shaped for external consumers—and the steps we took to expose only what was ready. Using Apollo Federation Contracts, we filtered out unstable or sensitive parts of the graph. Along the way, we defined best practices for the public schema, like cursor-based pagination, using oneOf for inputs and results. We’ll also touch on how we serve the schema through Hive Gateway with a supergraph setup, and the security measures we added, like depth limiting and complexity analysis. To keep things evolving safely, we rely on GraphQL Hive to track usage and guide deprecations.

If you’re thinking about exposing a GraphQL API—or just want ideas for keeping one clean and manageable—this talk will share what worked for us, what didn’t, and what we learned.

In this talk, we're unpacking GraphQL Fusion, a new approach that makes building distributed GraphQL APIs easier. We'll explore how this approach lets your teams work independently by owning different parts of the company's graph and cover how you can reshape subgraphs to follow your company's rules. Come learn about this powerful tool that brings efficiency and customization to your GraphQL operations.

How are you doing your tests? Are you effectively covering all use cases and their unique nuances? How many times have you broken everything? 

Let's delve into the complexities of testing a federated supergraph at scale, discuss on what are the issues and limits of testing GraphQL today and options for improving testing and increase confidence.

GraphQL has exploded in popularity since its public launch in 2015, and PostgreSQL is still going strong after almost 30 years of development, gaining market share and impressive features at an ever increasing rate.

These tools can pair beautifully, in particular because they're both strongly typed and declarative. In this talk, we'll learn how combining these two technologies can lead to massively increased software development and delivery speed, faster APIs, fewer bugs, and ultimately enable you to focus on delivering value on the frontend rather than maintaining three different layers of data models and associated logic on the backend (DB, application, API).